Causal Fair Machine Learning via Rank-Preserving Interventional Distributions

TL;DR

This paper introduces a causal fairness approach using rank-preserving interventional distributions to create a normative fair world, improving fairness in machine learning models by effectively identifying and mitigating discrimination.

Contribution

It proposes a novel rank-preserving interventional distribution method to define a fair world and a warping technique for fair decision-making in ML models, validated through simulations and empirical data.

Findings

Effectively identifies most discriminated individuals

Mitigates unfairness in decision-making models

Outperforms causally preprocessing data approach

Abstract

A decision can be defined as fair if equal individuals are treated equally and unequals unequally. Adopting this definition, the task of designing machine learning (ML) models that mitigate unfairness in automated decision-making systems must include causal thinking when introducing protected attributes: Following a recent proposal, we define individuals as being normatively equal if they are equal in a fictitious, normatively desired (FiND) world, where the protected attributes have no (direct or indirect) causal effect on the target. We propose rank-preserving interventional distributions to define a specific FiND world in which this holds and a warping method for estimation. Evaluation criteria for both the method and the resulting ML model are presented and validated through simulations. Experiments on empirical data showcase the practical application of our method and compare results with "fairadapt" (Ple\v{c}ko and Meinshausen, 2020), a different approach for mitigating unfairness by causally preprocessing data that uses quantile regression forests. With this, we show that our warping approach effectively identifies the most discriminated individuals and mitigates unfairness.